{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MindSpore Vision套件数据可视化\n",
    "\n",
    "为了方便对网络模型的训练损失、验证精度等结果进行展示，以及对不同模型之间的结果进行对比分析，数据可视化的工作就显得尤为重要, MindSpore Vision套件对外提供了相关的数据可视化API，可以方便开发者在做网络模型实验、学习、更好的助力科研等工作。\n",
    "\n",
    "下面将对MindSpore Vision套件所提供的数据可视化API的功能以及应用场景进行介绍。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实验数据可视化\n",
    "\n",
    "### topn_accuracy_chart\n",
    "\n",
    "函数`topn_accuracy_chart`主要用于绘制在不同AI框架下，模型精度的对比。\n",
    "\n",
    "#### 参数说明：\n",
    "\n",
    "- `accuracy_data`：用于绘制折线图的模型数据，其中数据包括不同AI框架下的模型精度。\n",
    "- `save_path`：图表的保存路径。\n",
    "- `ylim`：图表y坐标的刻度范围。\n",
    "- `figsize`：图表的尺寸。\n",
    "- `title`：图表的标题。\n",
    "- `xlabel`：图表x坐标的标签。\n",
    "- `ylabel`：图表y坐标的标签。\n",
    "\n",
    "#### 样例\n",
    "\n",
    "以下样例绘制了Resnet系列模型分别在MindSpore、Pytorch、Paper下的模型精度对比。结论如下：\n",
    "\n",
    "1. 基于MindSpore框架下训练的Resnet系列模型，在ImageNet验证数据集的精度要比Pytorch略高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from mindvision.utils.charts import * # pylint: disable=W0401\n",
    "\n",
    "accuracy_data = {\n",
    "    'MindSpore': {\n",
    "        'Resnet18': 70.078,\n",
    "        'Resnet34': 73.72,\n",
    "        'Resnet50': 76.6,\n",
    "        'Resnet101': 77.62,\n",
    "        'Resnet152': 78.638},\n",
    "    'Pytorch': {\n",
    "        'Resnet18': 69.758,\n",
    "        'Resnet34': 73.31,\n",
    "        'Resnet50': 76.13,\n",
    "        'Resnet101': 77.374,\n",
    "        'Resnet152': 78.312},\n",
    "    'Paper': {\n",
    "        'Resnet18': 72.15,\n",
    "        'Resnet34': 74.97,\n",
    "        'Resnet50': 77.15,\n",
    "        'Resnet101': 78.25,\n",
    "        'Resnet152': 78.57}\n",
    "}\n",
    "\n",
    "topn_accuracy_chart(accuracy_data=accuracy_data,\n",
    "                    figsize=(8, 6),\n",
    "                    title='Top-1 Accuracy (MindSpore vs Pytorch vs Paper)',\n",
    "                    xlabel='Resnet Models',\n",
    "                    ylabel='ImageNet Top-1 Accuracy(%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### accuracy_on_dataset_chart_v1\n",
    "\n",
    "函数`accuracy_on_dataset_chart_v1`主要用于绘制在不同预训练数据集上模型精度的区间范围。\n",
    "\n",
    "#### 参数说明：\n",
    "\n",
    "- `accuracy_data`：用于绘制折线图的模型数据，其中数据包括不同预训练数据集的模型精度和marker的大小。\n",
    "- `save_path`：图表的保存路径。\n",
    "- `ylim`：图表y坐标的刻度范围。\n",
    "- `figsize`：图表的尺寸。\n",
    "- `title`：图表的标题。\n",
    "- `xlabel`：图表x坐标的标签。\n",
    "- `ylabel`：图表y坐标的标签。\n",
    "\n",
    "#### 样例\n",
    "\n",
    "以下样例绘制了Resnet系列模型分别在ImageNet、ImageNet21K、JFT-300M预训练数据集训练，然后在ImageNet数据集上进行finetune后的Top1精度的区间范围。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_data = {\n",
    "    'ResNet50': {\n",
    "        'accuracy': {\n",
    "            'ImageNet': 76.8,\n",
    "            'ImageNet21K': 80.2,\n",
    "            'JFT-300M': 79.2,\n",
    "        },\n",
    "        'marker_size': 4\n",
    "    },\n",
    "    'ResNet152': {\n",
    "        'accuracy': {\n",
    "            'ImageNet': 81.2,\n",
    "            'ImageNet21K': 85.5,\n",
    "            'JFT-300M': 87.8,\n",
    "        },\n",
    "        'marker_size': 6\n",
    "    }\n",
    "}\n",
    "\n",
    "accuracy_on_dataset_chart_v1(accuracy_data=accuracy_data,\n",
    "                             ylim=[69.5, 90],\n",
    "                             figsize=(8, 6),\n",
    "                             xlabel='Pre-training dataset',\n",
    "                             ylabel='ImageNet Top1 Accuracy[%]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### accuracy_on_dataset_chart_v2\n",
    "\n",
    "函数`accuracy_on_dataset_chart_v2`主要用于绘制在不同预训练数据集上模型精度的对比。\n",
    "\n",
    "#### 参数说明：\n",
    "\n",
    "- `accuracy_data`：用于绘制点状图的模型数据，其中数据包括不同预训练数据集的模型精度和marker的大小。\n",
    "- `save_path`：图表的保存路径。\n",
    "- `ylim`：图表y坐标的刻度范围。\n",
    "- `figsize`：图表的尺寸。\n",
    "- `title`：图表的标题。\n",
    "- `xlabel`：图表x坐标的标签。\n",
    "- `ylabel`：图表y坐标的标签。\n",
    "\n",
    "#### 样例\n",
    "\n",
    "以下样例绘制了ViT系列模型分别在ImageNet、ImageNet21K、JFT-300M预训练数据集训练，然后在ImageNet数据集上进行finetune后的Top1精度对比。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_data = {\n",
    "    'ViT-B_16': {\n",
    "        'accuracy': {\n",
    "            'ImageNet': 77.91,\n",
    "            'ImageNet21K': 80.99,\n",
    "            'JFT-300M': 84.15,\n",
    "        },\n",
    "        'marker_size': 100\n",
    "    },\n",
    "    'ViT-B_32': {\n",
    "        'accuracy': {\n",
    "            'ImageNet': 73.38,\n",
    "            'ImageNet21K': 81.28,\n",
    "            'JFT-300M': 80.73,\n",
    "        },\n",
    "        'marker_size': 50\n",
    "    },\n",
    "    'ViT-L_16': {\n",
    "        'accuracy': {\n",
    "            'ImageNet': 76.53,\n",
    "            'ImageNet21K': 85.15,\n",
    "            'JFT-300M': 87.12,\n",
    "        },\n",
    "        'marker_size': 200\n",
    "    },\n",
    "    'ViT-L_32': {\n",
    "        'accuracy': {\n",
    "            'ImageNet': 71.16,\n",
    "            'ImageNet21K': 83.97,\n",
    "            'JFT-300M': 84.37,\n",
    "        },\n",
    "        'marker_size': 150\n",
    "    },\n",
    "\n",
    "    'ViT-H_14': {\n",
    "        'accuracy': {\n",
    "            'JFT-300M': 88.12,\n",
    "        },\n",
    "        'marker_size': 250\n",
    "    }\n",
    "}\n",
    "\n",
    "accuracy_on_dataset_chart_v2(accuracy_data=accuracy_data,\n",
    "                             ylim=[69.5, 90],\n",
    "                             figsize=(8, 6),\n",
    "                             xlabel='Pre-training dataset',\n",
    "                             ylabel='ImageNet Top1 Accuracy[%]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### accuracy_on_dataset_chart_v3\n",
    "\n",
    "函数`accuracy_on_dataset_chart_v3`主要用于绘制在不同预训练数据集上不同模型架构之间的精度对比。\n",
    "\n",
    "#### 参数说明：\n",
    "\n",
    "- `line_models_data`：用于绘制折线图的模型数据，其中数据包括不同预训练数据集的模型精度和marker的大小。\n",
    "- `scatter_models_data`：用于绘制点状图的模型数据，其中数据包括不同预训练数据集的模型精度和marker的大小。\n",
    "- `save_path`：图表的保存路径。\n",
    "- `ylim`：图表y坐标的刻度范围。\n",
    "- `figsize`：图表的尺寸。\n",
    "- `title`：图表的标题。\n",
    "- `xlabel`：图表x坐标的标签。\n",
    "- `ylabel`：图表y坐标的标签。\n",
    "\n",
    "#### 样例\n",
    "\n",
    "以下样例绘制了Resnet系列模型和ViT系列模型分别在ImageNet、ImageNet21K、JFT-300M预训练数据集训练，然后在ImageNet数据集上进行finetune后的Top1精度对比。结论如下：\n",
    "\n",
    "1. 其中图表的阴影部分代表了ResNet50和Resnet152的精度范围。\n",
    "2. ViT系列的模型在中小型数据集预训练的效果不好，精度全面不如ResNet系列的模型。\n",
    "3. 随着预训练数据集的增大，ViT系列的模型精度表现越来越好。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "line_models_data = {\n",
    "    'ResNet50': {\n",
    "        'accuracy': {\n",
    "            'ImageNet': 76.8,\n",
    "            'ImageNet21K': 80.2,\n",
    "            'JFT-300M': 79.2,\n",
    "        },\n",
    "        'marker_size': 4\n",
    "    },\n",
    "    'ResNet152': {\n",
    "        'accuracy': {\n",
    "            'ImageNet': 81.2,\n",
    "            'ImageNet21K': 85.5,\n",
    "            'JFT-300M': 87.8,\n",
    "        },\n",
    "        'marker_size': 6\n",
    "    }\n",
    "}\n",
    "\n",
    "scatter_models_data = {\n",
    "    'ViT-B_16': {\n",
    "        'accuracy': {\n",
    "            'ImageNet': 77.91,\n",
    "            'ImageNet21K': 80.99,\n",
    "            'JFT-300M': 84.15,\n",
    "        },\n",
    "        'marker_size': 100\n",
    "    },\n",
    "    'ViT-B_32': {\n",
    "        'accuracy': {\n",
    "            'ImageNet': 73.38,\n",
    "            'ImageNet21K': 81.28,\n",
    "            'JFT-300M': 80.73,\n",
    "        },\n",
    "        'marker_size': 50\n",
    "    },\n",
    "    'ViT-L_16': {\n",
    "        'accuracy': {\n",
    "            'ImageNet': 76.53,\n",
    "            'ImageNet21K': 85.15,\n",
    "            'JFT-300M': 87.12,\n",
    "        },\n",
    "        'marker_size': 200\n",
    "    },\n",
    "    'ViT-L_32': {\n",
    "        'accuracy': {\n",
    "            'ImageNet': 71.16,\n",
    "            'ImageNet21K': 83.97,\n",
    "            'JFT-300M': 84.37,\n",
    "        },\n",
    "        'marker_size': 150\n",
    "    },\n",
    "\n",
    "    'ViT-H_14': {\n",
    "        'accuracy': {\n",
    "            'JFT-300M': 88.12,\n",
    "        },\n",
    "        'marker_size': 250\n",
    "    }\n",
    "}\n",
    "\n",
    "accuracy_on_dataset_chart_v3(line_models_data=line_models_data,\n",
    "                             scatter_models_data=scatter_models_data,\n",
    "                             ylim=[69.5, 90],\n",
    "                             figsize=(8, 6),\n",
    "                             xlabel='Pre-training dataset',\n",
    "                             ylabel='ImageNet Top1 Accuracy[%]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### accuracy_model_size_chart\n",
    "\n",
    "函数`accuracy_model_size_chart`主要用于绘制在不同大小的预训练数据集上模型之间的精度对比。\n",
    "\n",
    "#### 参数说明：\n",
    "\n",
    "- `accuracy_data`：用于绘制折线图的模型数据，其中数据包括不同大小的预训练数据集的精度。\n",
    "- `size_unit`：数据集大小的单位。\n",
    "- `save_path`：图表的保存路径。\n",
    "- `ylim`：图表y坐标的刻度范围。\n",
    "- `figsize`：图表的尺寸。\n",
    "- `title`：图表的标题。\n",
    "- `xlabel`：图表x坐标的标签。\n",
    "- `ylabel`：图表y坐标的标签。\n",
    "\n",
    "#### 样例\n",
    "\n",
    "以下样例绘制了Resnet系列模型和ViT系列模型分别在10M、30M、100M、300M的JFT-300M预训练数据集训练，将ImageNet数据集1000类中的每一类随机采样5张图片作为验证数据集，然后将不同大小预训练数据集的预训练模型作为特征提取层，在验证数据集上直接进行精度对比。结论如下：\n",
    "\n",
    "1. 在预训练数据集比较少的情况下ViT系列模型的精度不如ResNet系列。\n",
    "2. 随着预训练数据集的增多，ViT系列模型的精度越来越高，效果要比ResNet系列要好。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_data = {\n",
    "    'ResNet50': {\n",
    "        10: 48,\n",
    "        30: 51,\n",
    "        100: 52,\n",
    "        300: 52.5\n",
    "    },\n",
    "    'ResNet152': {\n",
    "        10: 53,\n",
    "        30: 67,\n",
    "        100: 68,\n",
    "        300: 69\n",
    "    },\n",
    "    'ViT-B_32': {\n",
    "        10: 37,\n",
    "        30: 41,\n",
    "        100: 41.5,\n",
    "        300: 42\n",
    "    },\n",
    "    'ViT-B_16': {\n",
    "        10: 38,\n",
    "        30: 53,\n",
    "        100: 54,\n",
    "        300: 55\n",
    "    },\n",
    "    'ViT-L_32': {\n",
    "        10: 36,\n",
    "        30: 54,\n",
    "        100: 64,\n",
    "        300: 65\n",
    "    },\n",
    "    'ViT-L_16': {\n",
    "        10: 42,\n",
    "        30: 58,\n",
    "        100: 70,\n",
    "        300: 72\n",
    "    }\n",
    "}\n",
    "\n",
    "accuracy_model_size_chart(accuracy_data=accuracy_data,\n",
    "                          size_unit='M',\n",
    "                          ylim=[28, 74],\n",
    "                          figsize=(8, 6),\n",
    "                          xlabel='Number of JFT pre-training samples',\n",
    "                          ylabel='Linear 5-shot ImageNet Top1 [%]')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### accuracy_model_flops_chart\n",
    "\n",
    "函数`accuracy_model_flops_chart`主要用于绘制在不同的计算复杂度下，不同架构的模型之间的精度对比。\n",
    "\n",
    "#### 参数说明：\n",
    "\n",
    "- `accuracy_data`：用于绘制点状图的模型数据，其中数据包括不同架构下的模型，以及模型的计算复杂度和精度。\n",
    "- `save_path`：图表的保存路径。\n",
    "- `ylim`：图表y坐标的刻度范围。\n",
    "- `figsize`：图表的尺寸。\n",
    "- `title`：图表的标题。\n",
    "- `xlabel`：图表x坐标的标签。\n",
    "- `ylabel`：图表y坐标的标签。\n",
    "\n",
    "#### 样例\n",
    "\n",
    "以下样例绘制了Transform、ResNet、Hybrid三种不同网络架构下的模型，在不同计算复杂度下的精度对比。其中所有的模型精度都是在JFT-300M预训练数据集进行预训练，然后在ImageNet数据集进行finetune后的Top1精度。结论如下：\n",
    "\n",
    "1. 在同等的计算复杂度下，Transformer架构的模型精度要比ResNet架构的模型精度高，所以ViT系列的模型比较便宜。\n",
    "2. 在模型比较小的情况下，Hybrid架构的模型精度最高，但是随着模型越来越大，精度就和Transformer架构的模型差不多，甚至不如它。\n",
    "3. Transformer架构的模型扩展性比较强，随着模型越来越大，精度没有呈现饱和的趋势。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qhnLgzuVytLW1NURDt52OvYm4mh0bc+SXT6b7cXVkH1f29pIdHh6zTQFPvuoDPLHsIoQCdMymvU34+s2DrD2xlzOO60VqWOlqNDbmaLxP+XyegYEBCoUCANu3bwdg27ZtlZW4WlpaWLx4MZFIxHM/JtDVDOXArXuQmtTQbadjbyKuZsfGHPnlk+l+XB3Zx/hBG8qD9oUU2rqKGwqQyhTv815/Z3GqzTNX683cZWOOxvuUy+XYtGkTqVSKSCRSubedzWbZuHEj+Xyerq4uFixYoDVw1yN2Xc1Q3uNuaZl52F41dNvp2JuIq9mxMUd++WS6H1dH9pNrm80Tyy6i0Fb94adMVnH9nTFS6YKWro05Gu9TR0cHq1evpr+/H6By5l3+3t/fz/HHH09HR8eM+jGB9vFs3IMAkMvlGqah207H3kRczY6NOfLLJ9P9uDqyn90HrUZUfkqbFoF7H05p6dqYo2o+tbe3c/TRR0+4h1ze7vU2jWl0NUM5cLuHasKLjTlyD6fVv11Y6ygz6wAKkanPKLM5xb7o1IP7eGzM0WQ+JRIJ0uk0kUiElpYWIpEI6XSaRCJhtJ+ZoKsZyoHbvcYSXmzMkXsdrP7twlpHHck9tOQzU9q0twrz+mq/xwt25mgynwYGBlBKsWTJEk4++WSWLFmCUoqBgQGj/cwE9zpYDbiJI8KLjTlyE7DUv11Y62j+trt4dOXnp7QpKFhxRJeWro05msynuXPnsnLlSubPnw/AsmXLKve9TfYzE5pqAhYRWSMi6wYHB8lms6TTaVKpFCMjIyQSCfL5PMPDwyilKi/Vl5dHi0ajKKUYHh4mn8+TSCQYGRkhlUqxd+9estksyWSSXC5HPB6nUChM0Ch/j8ViFAoF4vF4ReP5558nk8lU/ClrlBdEH68RjUYpFAo8++yz5HI5kslkTTENDw/XFFM6nWbfvn0ziimTyXiKKR6Pa8VU635Kp9Mz3k/jY3r22Wetiykajfqyn3SPveli2rdv34z30+DgoKeYdu3apbWfYrFYzftp7969dTn2/Kin9oMOAqBt0SKkvZ3OubNZ+uj/o6clxqz2ETrbcvR0ZmmN5JkzK01XB5yzuoWuzhatmPbs2WPd34ihoaGq+2n+/Pl0dnaOiam7u5v99tvPl2Ovlpiee+65CTFNhcx0+jc/WL58udqwYUOj3WgatmzZwtKlSxvthsMRaGyso+ne426hgOqcTXurUCjg+T1uR/0RkQeVUsurfRbKS+VDQ0MzXlPVq4ZuOx17E3E1OzbmyC+fTPfj6sg+LiydJVYjmS5w38Mp9kXzzOuLsOKILro8zpxmY46CWkdeNN0Zdwix8UzB4Qgaro4c9WSqM26r73HXi/J9hkZo6LbTsTcRV7NjY4788sl0P0Gvo2S6wPr74/xofYz198dJliYhsfEYsQ0bcxTUOvKi6c64Q4g7U3CEGaUUN6+Pcf0vY7S0QHZE0d6mf8/X1ZGjnrgz7nHEprgPVG8N3XY69ibianZszJFfPpnuJ6h1dPP6GNffGSMzokhlFPnS3N2ZkeL0nz/61S5PPoUJV0eN1QzlwN3d3d0wDd12OvYm4mp2bMyRXz6Z7ieIdZRMF7j+lzEy2epXGjNZxQ3rR7Tn7g4bro4aqxnKgTuZTDZMQ7edjr2JuJodG3Pkl0+m+wliHd33UJLp1nPo7cpqz90dNlwdNVYzlAO37mowJjV02+nYm4ir2bExR375ZLqfINbRvliB7MjUz/VEk63ac3eHDVdHjdUM5cDtVjUKLzbmyK0OVv92Zft5vS20t0394Fl3Z0F77u6w4eqosZqhnIDFrSMcXmzMkVuP21y7ajOHAUR6e8nHYsjcF1NY+5cptUfyoj13d9hwddRYTfuy73A4HB6pNmiPRg0+y9oTe+lor37W3dkuvP2NPZ5nFHM4/CCUZ9yFwsyfGPWqodtOx95EXM2OjTnyyyfT/QSpjmTUesdnHNcL8MJ73DlVmbv77BN6OWVFmyefwoSro8ZqhnLgbm2dedheNXTb6dibiKvZsTFHfvlkup8g1VEhkaj8LCKcubqPU47pqTp398jIiCefwoSro8Zq2pd9H8hkMrS1zey/aq8auu107E3E1ezYmCO/fDLdT5DqKNLXRyE19hWvWZ0tHPfPs435FCZszFFQ68iLZihv5JhYCN2rhm47HXsbF7e3DRtz5JdPpvsJUh2N7N1bN+0wYmOOglpHXjRDOXDH4/GGaei207E3EVezY2OO/PLJdD9BqqP2BQvqph1GbMxRUOvIi2YoB+7e3t6Gaei207E3EVezY2OO/PLJdD9BqqPswEDdtMOIjTkKah150QzlwB305QhNaYcRG3MU1OUIbayj9p6eqts7Dj54ys9N+BQmbMxRUOvIi6Zb1jOEuOUIHY6Z4+rIUU/csp7jsPFMwYS9jf8F24aNOQrqmYKro/BiY46CWkdeNK0euEVkjYisGxwcJJvNkk6nSaVSjIyMkEgkyOfzDA8Po5QiGo0CLyQgGo2ilGJ4eJh8Pk8ikWBkZIRUKkVnZyfZbJZkMkkulyMej1MoFCZolL/HYjEKhQLxeLyi0dXVRSaTqfhT1iivqzpeIxqNUigUaG1tJZfLkUwma4ppzpw5NcWUTqeZNWvWjGLKZDKeYorH41ox1bqf0un0jPfT+JhaW1uti6m3t9eX/aR77E0X0+zZs2e8n8oaujGJiNZ+6uvrq3k/dXR01OXYa6Z66ujosC6m7u5uX/aT7rFXS0xtbW0TYpqKUF4qj0aj9PX1NURDt52Ofa22Yb7EZ2Lfm8Yvn0z34+rI1ZFNBLWOJtOc6lJ5KCdg6anhAZV6aei207E3EVezYyJHyXSB+x5Ksi9WYF5vC68/chazZjC3tV/7zXQ/ro7Ci405CmodedG0+lJ5vTCxELpXDd12OvY2Lm5vGzPJkVKKm+6KcupHn+abPx7ke/81xDd/PMipH32am+4qXvry26dG9uPqKLzYmKOg1pEXzVCecXd2djZMQ7edjr2JuJqdmeTo5vUxrr8zRmbkhQE6lSn+fP2dxfthZ65+4XJXrWfmfu030/24OqoNpRS7d+9m/vz5iEy9FnhQsPFvTVDryItmKM+4p7vxX08N3XY69ibiana85iiZLnD9L2NkstXPqjNZxfV3xkilC9pn5n7tN9P9uDqqjWg0yr333lt5QKkZsPFvTVDryItmKM+4g7SqkVsdzCxec3TfQ0mmW+u+ReDeh1PsHcppnZkHdVUjV0e1sXPnTgAGBgaYM2eOUe1GYePfmqDWkRdN+7LvA0FaR9itx20WrznaFyuQHZn6HnY2p9j9fI6b7xo7aI+mfGb+1mN66CpdNg/qOsKujqqTz+cZGBio6Gzfvh2Abdu2MXt2cTWylpYWFi9eTCQSmVFfjcLGvzVBrSMvmqEcuE28AudVQ7edjn0QXu1rNF5zNK+3hfY2qZw5V6O9Vdg7mKv5zLy8pKRf+810P66OqpPL5di0aROpVIpIJFLRy2azbNy4kXw+T1dXFwsWLAjswG3j35qg1pEXzVDe43aX+MKL1xy9/shZTPdPcUHBAXNbazoz3xfNz9gnXdyl8vpoj6ejo4PVq1fT398PvHA2Vf7e39/P8ccfT0dHx4z6aSQ2/q0Jah150QzlwO0eqgkvXnM0q7OFtSf20tFe/angznZh7Qm9vHhehPa2qZ8cbm8V5vW9cKYV1IdqXB1NTnt7O0cffTTt7e1Vt7e1tc24j0Zi49+aoNaRF81QDtzuNZbwMpMcnXFcL2tP6KWjTejqECIR6OoQOtqEs0/o5Yzjems+M19xRJcRn3Rwr4PVR3syEokE6XSaSCRCS0sLkUiEdDpNIpEwot9IbPxbE9Q68qIZyoHbTRwRXmaSIxHhzNV9/OyKfi46fR7vXjOHi06fx8+v6OfM1X2ISM1n5l2j3ucO6sQRro6mZmBgAKUUS5Ys4eSTT2bJkiUopRjQWBvcVmz8WxPUOvKiGcq5ypVSM54IwauGbjsd+1ptwzzHsol9X0sfN6+Pcf0vY7S0FO9pt7cKhQKsPbF4Zj7aBz98qkc/ro6mrqPdu3cDMH/+/Cm3BRG/jlkdglpHk2m6ucrHEYvFZjxJvFcN3XY69ibianb8yFH5zPyUY3q47+EU+6J55vVFWHFE15gzbT99qkc/ro6mptrgHPQBu4yNf2uCWkdeNEN5xh12wnzG7XCYwtWRo55MdcYdynvcJhZC96qh207H3sbF7W3Dxhz55ZPpflwdhRcbcxTUOvKiGcqB28S0g141dNvp2DfLdIr1xMYc+eWT6X5q1VNKsWvXrqqTTLg6CiY25iiodeRFs64Dt4h8SEQeE5FHReRmEekUkWtFZJuIPFT6OrKePlTDxGT/XjV02+nYN9MiBvXCxhz55ZPpfmrVm2qRDVdHwcTGHAW1jrxo1u3hNBHpBy4EXqaUSonIT4DTSx//u1Lqlnr1PR29vb0N09Btp2Pvxadal55sFkzse9P45ZPpfmrVm2qRjWapo7BhY46CWkdeNCcduEXkyloElFIXTqPfJSIjwCzgGS3v6kQ8Hqenp6chGrrtdOx1bCe8sjSiaG8Tvn7zYNVXlpoFE/veNH75ZLqfyfR0FtlIJpOBrqOwYmOOglpHXjSnOuN+O3D1NO3fR/GsegJKqadF5CvATiAFrFdKrReRM4HPicingd8CH1NKZWr22ACzZs1qmIZuOx17Hdub18e0lp5sFkzse9P45ZPpfibT01lkI+h1FFZszFFQ68iL5lQD951KqcumaiwiB07x2VzgZOAgYAj4qYicDfxfYDfQDqwDPgp8pkr79wLvBVi4cCFbtmyZMhAdstnshDmE/dLQbadjX6vts8/tYfPmKEcuntxm8+Zn2NTfS1trc511m9j3pvHLJ9P9TKV38MEH8/TTT5NIJCgUCpVVsFpaWujp6aG/v59t27YFuo727t1r9O9SkHB1ZLYfXc1JB26l1LvGbxORg4EupdRjk9mM4k3ANqXUnlLbnwNHK6VuKH2eEZEfAB+epP91FAd2li9frky+LzkyMjLjSf69aui207Gv1fbJp7L8dcfcKZeo7OoQViTmVZaebBZM7HvT+OWT6X6m03vZy17GHXfcQSqVqmxrb2/n2GOPrdyGCXIdhfk9bldHZvvR1az5KSQR+RDwbeCrIvLtGprsBF4jIrOkWKVvBDaLyIKSngCnAI/W7K0hcrlcwzR02+nY12qbyijtpSebBRP73jR++WS6n+n0allkI8h1FGZszFFQ68iL5qQDt4gcOm7TSqXUaqXU8cBrphNWSj0A3AL8FdhU6msdcKOIbCpt2x+4XMtjA5h46Mqrhm47Hftabbs6RHvpyWbBxgfu/PLJdD/T6dWyyEaQ6yjM2JijoNaRF82p7nF/TUQeAy5TSiWA7SLyXWAE2FWLuFLqEuCScZvfoOVhHWhpmfnrTl41dNvp2Ndq+5L5bdpLTzYLJva9afzyyXQ/0+nNnTuXlStXVubnXrZsGf39/UZ8sqGOwoyNOQpqHXnRnNRaKXUS8GfgNyJyFvBvwE+AX1K8xB1Ywn6pvK1VtJeebBbcJT7/9ObPnz9hUY3x24JcR2HGxhwFtY68aE75l7k0ScobgKXAeuA5pdQdSqmsZw8twMQTgV41dNvp2OvYnnFcL2tP6KWjTejqECKR4iX0jjbh7BOK73E3I7Y9CQv++WS6H1dH4cXGHAW1jrxoTjUBy4HAvwJZ4JvAtRQfTHsa+IRSasizlw0mnU7T3d3dEA3ddjr2Ora6S082Cyb2vWn88sl0P66OwouNOQpqHXnRnOoe903AlcBs4NrSpfNTROTNwJ3Aa2fiaCNxE7CMatPZ0nSvfE2FmzjCLr1mqaOwYWOOglpHXjSnOrWaA/wM+ClQuSmllLoDOMaDb9YwPDzcMA3ddjr2JuJqFq7s7eUrIhO+rjr4YL4iwpUWzbXs134z3Y+ro/BiY46CWkdeNKc6474S+DvFS+X/d/QHQb/H3dc386k8vWrottOxNxFXs5CdpBCyO3ZM+Xkj8Gu/me7H1VF4sTFHQa0jL5pTnXH/TCl1sFJqqVLqP6sZiMj+Wr1ZgomF0L1q6LbTsbdxcXvb6Dj44Ea7MAG/9pvpflwdhRcbcxTUOvKiOdXAvb6G9rXYWIeJhdC9aui207G3cXF728hs3dpoFybg134z3Y+ro/BiY46CWkdeNKcauJeKyCNTfG0Cxs+uFgjcmUJ4cWfcdum5OgomNuYoqHXkRXOqe9wnzMgTi3FnCuFlsjPuK3t7p7zv3d7Tw4WxWF18CuqZgquj8GJjjoJaR140p5o57Z5avmbqcCOIGfgD7FVDt52OvYm4mp32xdXXMp3uYbV6Pszm134z3Y+ro/BiY46CWkdeNJt3po0pMPHyvFcN3XY69rZNiGAj2V01TbPvK37tN9P9uDoKLzbmKKh15EUzlAN3MplsmIZuOx17E3E1C+09PVW3t+2//5SfNwK/9pvpflwdhRcbcxTUOvKiOdU9bgBE5JVKqU2ePbKQjo6OhmnottOxNxFXszDZ/WjdBev9wK/9ZrofV0fhxcYcBbWOvGjWcsZ9i4j8QUTOFhH7Zpb3QNhXBwszNuYoqKsauToKLzbmKKh15EVz2oFbKXUo8CngzcA2EfmSiLzUm3t2EPb1uMOMjTkK6jrCro7Ci405CmodedGsyVop9d9KqdOBtcBpwGYR+YWILNV3sXZEZI2IrBscHCSbzZJOp0mlUoyMjJBIJMjn8wwPD6OUIhqNAi+8DxeNRlFKMTw8TD6fJ5FIMDIyQiqVIpPJkM1mSSaT5HI54vE4hUJhgkb5eywWo1AoEI/HKxrZbJZMJlPxp6xRfjpwvEY0GqVQKJBIJMjlciSTSaMxpdPpGceUyWQ8xRSPxwMTUyKRqBpT+f3u9gMPhJYW2hYuRDo6aD3gAFp6eojMmWNtTEE+9nK5nKeYhoeH63bspdNpV08uJl+PvWQyOSGmqRCl1NQGIj3AWcB7gFnAt4HrKL7n/Sml1MunFDDA8uXL1YYNG4zppVIpurq6GqKh207HvlbbLVu2sHRpXf/nspbJctTI97hNHI+N6MfVkasjmwhqHU2mKSIPKqWWV7Of9uE04GngN8BHlVK/GbX9RyLyPs+eNpDW1lrCro+GbjsdexNxNTuT5aheg3It+LXfTPfj6ii82JijoNaRF81aLpW/HPiwUuo3ItItIq8qf6CU+l+a/llBJpNpmIZuOx17E3E1OzbmyC+fTPfj6ii82JijoNaRF81aBu5TgFtLPx8A/ExE3q3Vi2WYWAjdq4ZuOx17Gxe3tw0bc+SXT6b7cXUUXmzMUVDryItmLQP3+4DXAyiltgGvAi7S9swi4vF4wzR02+nYm4ir2bExR375ZLofV0fhxcYcBbWOvGjWMnBHlFKVG4BKqSgw9RNtltPb29swDd12OvYm4mp2bMyRXz6Z7sfVUXixMUdBrSMvmrUM3FtE5IsicrCIHCQinwH+x5N3luCWIwwvNuYoqMsRujoKLzbmKKh15EWzloH7/cA/AhuBDaWfP6DrmE245QjDi405CupyhK6OwouNOQpqHXnRrGXmtGeVUm9TSvUppfZTSp2ulNrj1UEbcGcK4cXGHAX1TMHVUXixMUdBrSMvmrUsMvIPwAeBbkCACHCIUup1HvyzAnemEF5szFFQzxRcHYUXG3MU1DryolnLpfKbgHbgaGA78DIg0KuFlaefa4SGbjsdexNxNTs25sgvn0z34+oovNiYo6DWkRfNWgbuHqXUB4C7gDuBY4Fl+q7ZQ4+BtZi9aui207E3EVezY2OO/PLJdD+ujsKLjTkKah150axl4N5X+v4E8Aql1BDFy+WBxcRC6F41dNvp2Nu4uL1t2Jgjv3wy3Y+ro/BiY46CWkdeNGuZIPV/ROQbFBcW+Z6IdAP2raKuQWdnZ8M0dNvp2JuIq9mxMUd++WS6H1dH4cXGHAW1jrxo1nLG/QHgXqXURuC7wBuA9+q7Zg/TLZlWTw3ddjr2JuJqdmzMkV8+me7H1VF4sTFHQa0jL5q1nHHfrpR6I4BS6tsUl/UMNG5Vo/BiY46CuqqRq6PwYmOOglpHXjRrOeOeIyKzvbljJ4VCoWEauu107E3E1ezYmCO/fDLdj6uj8GJjjoJaR140axnmE8AOEXkEqMyErpR6i55r9qDUzKda96qh207H3kRczY6NOfLLJ9P9uDoKLzbmKKh15EWzloH7e95csRd3iS+82JijoF7ic3UUXmzMUVDryIvmtNZKqes8e2Mp2WyWtra2hmjottOxNxFXs2NjjvzyyXQ/ro7Ci405CmodedGsZcrTYaos46mUsm9dtxpxr7GEFxtzFNTXWFwdhRcbcxTUOvKiWcvDaa8AXln6WgZ8DviMtmceEJE1IrJucHCQbDZLOp0mlUoxMjJCIpEgn88zPDyMUqoyZVx5svZoNIpSiuHhYfL5PIlEgpGREVKpFGW9ZDJJLpcjHo9TKBQmaJS/x2IxCoUC8Xi8ojE0NEQmk6n4U9aIxWJVNaLRKIVCgb1795LL5UgmkzXFlEwma4opnU5X7L3GlMlkPMUUj8e1Yqp1P6XT6Rnvp/Ex7d2717qY4vG4L/tJ99ibLqZoNDrj/RSLxTzF9Nxzz2ntp7K/teynwcHBuhx7zVRP+/btsy6m4eFhX/aT7rFXS0zPP//8hJimQrzcaBeRB5RS/6zd0CPLly9XGzZsMKanlEJEGqKh207HvlbbLVu2sHTp0pp9aCZM7HvT+OWT6X5cHbk6somg1tFkmiLyoFJqeTX7Ws64x4stBV7szT07KP8n1QgN3XY69ibianZszJFfPpnux9VReLExR0GtIy+auve4W4A24CPanllEX19fwzR02+nYm4ir2bExR375ZLofV0fhxcYcBbWOvGjq3uN+GXCAUuqb+q7Zg4mF0L1q6LbTsbdxcXvbsDFHfvlkuh9XR+HFxhwFtY68aNYycPcB31JK7Sj9/HsROVTfNXswsRC6Vw3ddjr2Ni5ubxs25sgvn0z34+oovNiYo6DWkRfNWgbubwPXACilNgGXAt/R9MsqTCyE7lVDt52OvY2L29uGjTnyyyfT/bg6Ci825iiodeRFs5aBe7ZS6j/LvyilbgUC+w43QG/vzN33qqHbTsfeRFzNjo058ssn0/24OgovNuYoqHXkRbOWgVuJyCvLv4jIYUBe0y+riMfj0xvVSUO3nY69ibiaHRtz5JdPpvtxdRRebMxRUOvIi2YtE6R+iuJ97U0Uny4/DDhL3zV7mDVrVsM0dNvp2JuIq9mxMUd++WS6H1dH4cXGHAW1jrxoTnvGrZS6AzgU+DrwFeDlSqlfe/LOEtLpdMM0dNvp2JuIq9mxMUd++WS6H1dH4cXGHAW1jrxoTjtwi8jhwA9K97m3A78O+lPl7e3tDdPQbadjbyKuZsfGHPnlk+l+XB2FFxtzFNQ68qIZyqfKc7lcwzR02+nYm4ir2bExR375ZLofV0fhxcYcBbWOvGiG8qlyE/PMetXQbadjb9vcwTZiY4788sl0P66OwouNOQpqHXnRDOVT5S0t2lO0G9PQbadjbyKuZkUpxa5du6z8g+PXfjPdj6uj8GJjjoJaR140a7EuP1X+exH5PXAP8HEPvlmDu8QXPqLRKPfeey+Dg4ONdmUCQb3E5+oovNiYo6DWkRfNaV8HU0rdUXoY7XVADnhAKfWcN/fswD1UEz527twJwLPPPsv8+fMb7M1YgvpQjauj8GJjjoJaR140az0/7wf2AlHg5SLyHk2/rMK9xtL85PN5tm/fztatW9m6dSvbt28HmLAtn2/8XZ+gvsbi6ii82JijoNaRF81alvX8LnAy0AU8DRwC3Ad814N/VuAmjmh+crkcmzZtIpVKEYlEUKq4Mu3IyAgbN24kn8/T1dXFggULiEQiDfU1qBNHuDoKLzbmKKh15EWzljPuY4GDgJ8DJwFvBJLanlnE8PBwwzR02+nYm4irnpQfECsPovWko6OD1atX09/fD0ChUKj4ANDf38/xxx9PR0dH3X2ZDr/2m+l+XB2FFxtzFNQ68qJZy8C9SymVALYAr1RK3QPs78E3azCxELpXDd12OvY2Lm4/mvIDYn6t4tPe3s7RRx895v6RUqqyva2tzRc/psOv/Wa6H1dH4cXGHAW1jrxo1jJwZ0VkJfA4cLyI9BHwgdvEQuheNXTb6djbuLj9aMoPiA0MDPjWZyKRIJ1OE4lEaGlpIRKJkE6nSSQSvvkwHX7tN9P9uDoKLzbmKKh15EWzloH7o8D7gF8Cr6L4kNoNuo55QUTWiMi6wcFBstks6XSaVCrFyMgIiUSCfD7P8PAwSqnKWVw5AdFoFKUUw8PD5PN5EokEIyMjpFIpOjs7yWazJJNJcrkc8XicQqEwQaP8PRaLUSgUiMfjFY2uri4ymUzFn7JGLBarqhGNRikUCrS2tpLL5UgmkzXFNGfOnJpiSqfTzJo1a0YxZTIZTzHF4/GqMaXTaf72t7/xxBNP8Pjjj7Nt2zZEhG3btvHYY4+xdetWNm/eTC6XmzSmme6nbdu2oZRi0aJFnHjiiSxatAilFE888YSnmGZ67FWLqbe315f9pHvsTRfT7NmzZ7yfyhq6MYmI1n7q6+ureT91dHQYOfZM11M9jj2vMXV0dFgXU3d3ty/7SffYqyWmtra2CTFNhejebxSROUqpIa1GM2T58uVqw4YNxvSGhoaYM2dOQzR02+nY12q7ZcsWli5dWrMPXslkMqxfv37MA2KFQoGWlhZEpPKA2HHHHVe3e827d+8GqLwCNjQ0VHmC05bXwkwcj43ox9WRP3VkI34dszoEtY4m0xSRB5VSy6vZaw/cjcD0wB12/PyDk81m+ctf/sLu3bvHvHoViUSYP38+Rx11lDX3mh0OHcI8cDvqz1QDt33z1vlA+RJIIzR02+nYm4jLNNUeEBu93e9B28Yc+eWT6X5cHYUXG3MU1DryolnLsp4v9uyNpXR3dzdMQ7edjr2JuOqBTQ+I2Zgjv3wy3Y+ro/BiY46CWkdeNGs5477Hmyv2kkzO/DV0rxq67XTsTcRVDwYGBlBKsWTJEk4++WSWLFmCUsrXp8vL2Jgjv3wy3Y+ro/BiY46CWkdeNKedOQ3YISJHA/crpQqevLIMEw9CedXQbadjb8NkItWYO3cuK1eurDwMtmzZssrEKH5jY4788sl0P66OwouNOQpqHXnRrOWM+zCKU5ymRCQmIsMiYt8NDg3cqkb+Mn/+/AlPcFfb5gc25iioqxq5OgovNuYoqHXkRbOWM+4V3lyxF7eOcHixMUdBXUfY1VF4sTFHQa0jL5rTWiuldgD/BLwH2AMcXdrmcDgcDofDZ2p5qvxjwAeAd1BcIewSEflUvR2rJ+UFJxqhodtOx95EXM2OjTnyyyfT/bg6Ci825iiodeRFs5bz89OBE4GEUup54DXAmfqu2UNray13COqjodtOx95EXM2OjTnyyyfT/bg6Ci825iiodeRFs5aBe0QplSn/UprudETPLbvIZDLTG9VJQ7edjr2JuJodG3Pkl0+m+3F1FF5szFFQ68iLZi3D/ICInAQoEekAPgwE+h63iYXQvWrottOxt3Fxe9uwMUd++WS6H1dH4cXGHAW1jrxo1nLG/UHg34DDgQRwAvCv2p5ZRDweb5iGbjsdexNxNTs25sgvn0z34+oovNiYo6DWkRfNSQduEfls6ceDlFJvBHqBuUqp1yuldnp3sfH09vY2TEO3nY69ibiaHRtz5JdPpvtxdRRebMxRUOvIi+ZUZ9xnishC4FsiMhfoBNpEZJ6IzJuBjw3HxELoXjV02+nY+7WQfJCxMUd++WS6H1dH4cXGHAW1jrxoTnWPez1Qnkz6+XGfKSCi1ZNFmFhL1auGbjsde9vWx7URG3Pkl0+m+3F1FF5szFFQ68iL5qRn3EqpDyilIsAflFIt474CO2iDO1MIMzbmKKhnCq6OwouNOQpqHXnRrOXhtGNE5GAAEXmbiHxaRPo8+GYN7kwhvNiYo6CeKbg6Ci825iiodeRFs5aB+2rgoyLyMuAq4CDge9qeWUQ0Gm2Yhm47HXsTcTU7NubIL59M9+PqKLzYmKOg1pEXzVoG7mUUpzw9BbhOKXU+cKC2Zx4QkTUism5wcJBsNks6nSaVSjEyMkIikSCfzzM8PIxSqhJ4+ZJDNBpFKcXw8DD5fJ5EIsHIyAipVIq2tjay2SzJZJJcLkc8HqdQKEzQKH+PxWIUCgXi8XhFo729nUwmU/GnrBGLxapqRKNRCoUCIkIulyOZTNYUU09PT00xpdNpOjo6ZhRTJpPxFFM8HteKqdb9lE6nZ7yfxsfU0tJiXUyzZ8/2ZT/pHnvTxdTZ2Tnj/VTW0I1JKaW1n7q7u2veT21tbXU59pqpnlpbW62Lqaury5f9pHvs1RJTJBKZENNUiFJqagORvyqlXi0ivwM+r5T6tYg8rpR62ZQNDbJ8+XK1YcMGY3rxeJzu7u6GaOi207Gv1XbLli0sXbq0Zh+aCRP73jR++WS6H1dHro5sIqh1NJmmiDyolFpezb6WmdOeEJFfAgcDd4vIjcAjM/a0gXR2dk7YdmVvL9nh4UnbtPf0cGHshWXIq2l47duUvVefwoSNOfLLJ9P9mNBzdRRMbMxRUOvIi2Ytl8rPB24CjlFKjQD3Au/Sd80eql2GmGrQrvb5dJcydPo2ZV/NVinFrl27mO7KSljwut/qiV8+me7HhF5Q6sgxFhtzFNQ68qJZy3rcCYqD9VwReTXwZyDQ14fCtKpRNBrl3nvvtfJhkkbgVjWySy8odeQYi405CmodedGc1lpErgD+N/AcxYlXKH0/WNc5WwjTOsI7dxZnpx0YGLDyFQ6/cesI26UXlDpyjMXGHAW1jrxo1jLMvwM4RCn1jCePLMTEZWOvGrrtdOyVUuTzeQYGBioHwvbt2wHYtm0bs2fPBopPNpafZAwbNt4y8Msn0/00cx05psbGHAW1jrxo1rSsZzMN2tDcl/hyuRybNm0ilUoRiUQqB0Q2m2Xjxo3k83k6OjrI5XKhHLjdJT679GytI8fU2JijoNaRF81aHk77rYh8SUReJyKvLn95c88Omvmhmo6ODlavXk1/fz/wwiWY8vf+/n4OOeQQOjo6tPxoFtxDNXbp2VpHjqmxMUdBrSMvmrUM3OcBbwduAH5W+rpF1zGbaPbXWNrb2zn66KNpb28f83l5exjPtMu411js0rO5jhyTY2OOglpHXjRrear8oCpfgX0wDSCZTE7Y1t7TM2Wb8Z9X0/Datyn70baJRIJ0Ok0kEqGlpYVIJEI6nSaRSGj132x43W/1xC+fTPdjQs/2OnJUx8YcBbWOvGjW8lT5/sBaoBsQist5HqKUOsuLgzZQbdab0ZOreNWoRzsd+9G2AwMDKKVYsmQJhx9+OI888ghPPvkkAwMDiIiWD82EbbM9gX8+me7HhJ7tdeSojo05CmodedGs5VL5T4A3Ae8GFgPnAva9C6BBTHOQNqmh207HfrTt3LlzWblyJcuWLaOtrY1ly5axcuVK5s6dq9V/s2Fi35vGL59M9xOGOnJUx8YcBbWOvGjWMlf5k0qpl4rIt4DvAHuBW5RSr/XspSam5yoPO2GeY9nhMIWrI0c9mWqu8lrOuHeXvv8P8Aql1NPU9hqZtZhYCN2rhm47HXsbF7e3DRtz5JdPpvtxdRRebMxRUOvIi2YtA/BzIvLvwJ+Ay0QkBvTpu2YPJmYQ86qh207H3s2MNj025sgvn0z34+oovNiYo6DWkRfNWs643wdklFL3ARuAzwAf1fbMIkzM2+1VQ7edjr2bj3x6bMyRXz6Z7sfVUXixMUdBrSMvmrWccX9FKXUOgFLqowR80Abo7e1tmIZuOx17E3E1OzbmyC+fTPfj6ii82JijoNaRF81azriPkCZ7fygejzdMQ7edjr2JuJodG3Pkl0+m+3F1FF5szFFQ68iLZi1n3LuAx0TkfqCirpS6cLqGIvIh4AKKq4ltori29wLgR8B+wIPAWqWUr/PnzZo1q2Eauu107E3E1ezYmCO/fDLdj6uj8GJjjoJaR140Jz3jFpHyZNZ/An4M7ACeH/U1JSLSD1wILFdKvYLixC2nA1cAX1dKHQIMUnw/3FfS6XTDNHTb6dibiKvZsTFHfvlkuh9XR+HFxhwFtY68aE51xv0n4NVKqctm4E8r0CUiI8AsimfvbwDOLH1+HXAp8O0Z9KHN+Dm8/dTQbadjbyKuZsfGHPnlk+l+XB2FFxtzFNQ68qI51cA9o/vaSqmnReQrwE4gBayneGl8SCmVK5k9BfRX7VzkvcB7ARYuXMiWLVtm4s4YRkZGaGtra4iGbjsd+1pt9+7dazSfQcLEvjeNXz6Z7sfVkasjmwhqHXnRnGrg7hSRVzHJAK6U+utUwiIyFzgZOAgYAn4KHF+rY0qpdcA6KM6cZnKGonQ6PeMVXrxq6LbTsa/VNswzPpnY96bxyyfT/bg6cnVkE0GtIy+aUw3cB1NcwrPawK1Kn0/Fm4BtSqk9ACLyc+B1wBwRaS2ddS8Cnq7ZW0O0tNTyMH19NHTb6dibiKvZsTFHfvlkuh9XR+HFxhwFtY68aE5l/bhS6uAZLOu5E3iNiMwqvU72RuBx4HfAaSWbc4HbtDw2QC6Xm96oThq67XTsTcTV7NiYI798Mt2Pq6PwYmOOglpHXjTr9i+KUuoB4BbgrxRfBWuheOn7o8C/icgTFF8J+169fJgM91BNeLExR0F9qMbVUXixMUdBrSMvmlMN3L+fmSuglLpEKbVUKfUKpdRapVRGKbVVKXWUUuoQpdTblVKZmfaji3uNJbzYmKOgvsbi6ii82JijoNaRF81JB26l1EUz9sZS3MQR4cXGHAV14ghXR+HFxhwFtY68aNr3hIEPDA8PN0xDt52OvYm4mh0bc+SXT6b7cXUUXmzMUVDryItmKAfuvr6Zr0rqVUO3nY69ibiaHRtz5JdPpvtxdRRebMxRUOvIi2YoB24TC6F71dBtp2Nv4+L2tmFjjvzyyXQ/ro7Ci405CmodedEM5cBtYiF0rxq67XTsbVzc3jZszJFfPpnux9VReLExR0GtIy+aoRy43ZlCeLExR0E9U3B1FF5szFFQ68iLZigHbnemEF5szFFQzxRcHYUXG3MU1DryohnKgTsWizVMQ7edjr2JuJodG3Pkl0+m+3F1FF5szFFQ68iLZigH7u7u7oZp6LbTsTcRV7NjY4788sl0P66OwouNOQpqHXnRDOXAnUwmG6ah207H3kRczY6NOfLLJ9P9uDoKLzbmKKh15EUzlAN3R0dHwzR02+nYm4ir2bExR375ZLofV0fhxcYcBbWOvGiGcuB2qxqFFxtzFNRVjVwdhRcbcxTUOvKiGcqB260jHF5szFFQ1xF2dRRebMxRUOvIi6Z92Xc4HA6HwzEpoRy4C4VCwzR02+nYm4ir2bExR375ZLofV0fhxcYcBbWOvGiGcuBubW1tmIZuOx17E3E1OzbmyC+fTPfj6ii82JijoNaRF02rB24RWSMi6wYHB8lms6TTaVKpFCMjIyQSCfL5PMPDwyiliEajwAtTx0WjUZRSDA8Pk8/nSSQSjIyMkEqliMViZLNZkskkuVyOeDxOoVCYoFH+HovFKBQKxOPxisbw8DCZTKbiT1mj/CL9eI1oNEqhUGBwcJBcLkcymawppkwmU1NM6XSaeDw+o5gymYynmOLxuFZMte6ndDo94/00PqbBwUHrYkqlUr7sJ91jb7qYyr7PZD8lEglPMe3du1drP6XT6Zr3UywWq8ux10z1FI1GrYspmUz6sp90j71aYhoaGpoQ01SIUmpKAxtYvny52rBhgzG9QqEw4wcMvGrottOxr9V2y5YtLF26tGYfmgkT+940fvlkuh9XR66ObCKodTSZpog8qJRaXs3ersz7RDweb5iGbjsdexNxNTs25sgvn0z34+oovNiYo6DWkRfNUA7cvb29DdPQbadjbyKuZsfGHPnlk+l+XB2FFxtzFNQ68qIZyoHbLUcYXmzMUVCXI3R1FF5szFFQ68iLZigHbrccYXixMUdBXY7Q1VF4sTFHQa0jL5qhHLjdmUJ4sTFHQT1TcHUUXmzMUVDryItmKAdud6YQXmzMUVDPFFwdhRcbcxTUOvKiGcqBu/xOXSM0dNvp2JuIq9mxMUd++WS6H1dH4cXGHAW1jrxohnLg7unpaZiGbjsdexNxNTs25sgvn0z34+oovNiYo6DWkRfNUA7cJhZC96qh207H3sbF7W3Dxhz55ZPpflwdhRcbcxTUOvKiGcqBu7Ozs2Eauu107E3E1ezYmCO/fDLdj6uj8GJjjoJaR140QzlwTzcPbD01dNvp2JuIq9mxMUd++WS6H1dH4cXGHAW1jrxohnLgdqsahRcbcxTUVY1cHYUXG3MU1DryohnKgdutIxxebMxRUNcRdnUUXmzMUVDryItmKAduEyuiedXQbadjH4SV3hqNjTnyyyfT/bg6Ci825iiodeRFM5QDt7vEF15szFFQL/G5OgovNuYoqHXkRTOUA7d7qCa82JijoD5U4+oovNiYo6DWkRfNUA7c7jWW8GJjjoL6Gouro/BiY46CWkdeNEM5cLuJI8KLjTkK6sQRro7Ci405CmodedG070aFD3R3dzdMQ7edjr2JuJodG3Pkl0+m+3F15J1CocBTTz1FIpGoe18Oe5k9ezaLFi3SPuZCOXDHYjH6+voaoqHbTsfeRFzNjo058ssn0/24OvLO3r17EREOPfRQWlqCd+Ezl8tZ94CaXz6Z6qdQKPD000+zd+9eOjo6tI45q48YEVkjIusGBwfJZrOk02lSqRQjIyMkEgny+TzDw8MopSqrq5TXNY1GoyilGB4eJp/Pk0gkGBkZIZVK0dHRQTabJZlMksvliMfjFAqFCRrl77FYjEKhQDwer2h0dnaSyWQq/pQ1YrFYVY1oNEqhUCASiZDL5UgmkzXF1NfXV1NM6XSarq6uGcWUyWQ8xRSPx7ViqnU/pdPpGe+n8TFFIhHrYurp6fFlP+kee9PFNGvWrBnvp7KGbkyA1n7q7e2teT+1t7fX5dgbHdPQ0BD7778/LS0t5HI5oDggKKXI5/OV74VCYczX6M+UUmPamtQAptRoaWmZoFVNw8+YIpHIjGKq5k81DcBITCLC/vvvz9DQEK2trRP+RkyF2Pg+3niWL1+uNmzYYExvaGhoxmuqetXQbadjX6vtli1bWLp0ac0+NBMm9r1p/PLJdD+ujrzX0ebNm1m6dCki4ql9o3Fn3Gb6UUqxZcsWFixYMOGYE5EHlVLLq7Wz+oy7Xpj44+VVQ7edjr1tA5KN2Jgjv3wy3Y+ro5kR1EEb3HvcpigfA7rHXCgHbhMLoXvV0G2nY2/j4va2YWOO/PLJdD+ujsLL6MvGtuCXT/XoR/eYC+XA3dvb2zAN3XY69ibianZszJFfPpnux9VReIlEInXTfvbZZ1m5ciU9PT38n//zf3zx6Tvf+Q4XX3xxTbZPP/003d3dlXvgU3H77bfzzne+c1o73WMudAO3Uopt27bNeL7ZeDzuSzsde68+hQkbc+SXT6b7MaHn6sguuru7K18tLS10dXVVfr/xxhsrdvVc0GPdunXsv//+xGIxvvrVr9bczqtP2WyWyy+/nH//938nnU4zZ84c/vu//3uC3Yc+9CFOO+00Fi1aRDweJxKJ8PKXv7ySn0gkQmdnZ+X3z3/+86xZs4bHHnuMRx55ZEofdI85+25U1JloNMqGDRuYN2/ejO5lzZo1y5d2OvZefQoTNubIL59M92NCz9VR7VzZ20t2eHjSz9t7eriw9BS+V0YPIEuWLOGaa67hTW960wS78lsK9WDHjh287GUv034GoPyEvu496Ntuu42lS5fS398PwDvf+U5++MMf8oY3vKFik8/nufnmm/nud7875vW9xx57rPLzqlWrOPvss7ngggvG6J9xxhmsW7eOq666alIfdI+50J1x79y5E4CBgYEZ6aTTaV/a6dh79SlM2Jgjv3wy3Y8JPVdHtTPVoF3L5zPh7rvvZtGiRVxxxRXMnz+f888/n8HBQd785jdzwAEHMHfuXN785jfz1FNPVdqsWrWKT33qU7zuda+jp6eH4447jr179wLFHJ999tnst99+zJkzh3/6p3/i2Wef5bzzzuO6667jS1/6Et3d3fzmN78hk8lw8cUXs3DhQhYuXMjFF19MJpOZ4NeCBQs4//zzufTSS3n729/O2WefTU9PD6985Sv5+9//zhe+8AVe9KIXsXjxYtavX1/x88477+SYY46p/H7uuefys5/9bMxsZnfddReFQoETTjiBrVu3IiI13+tetWoVv/jFL6a00T3mmn7gzufzbN++na1bt7J161a2b98OwLZt28Zsq+V+xWja29s9+aPbTsfeq09hwsYc+eWT6X5M6Lk6Cg67d+9m37597Nixg+985zsUCgXOP/98duzYwc6dO+nq6uKDH/zgmDY33XQTP/jBD3juuefIZrN85StfAeC6664jGo0yMDDA888/z9VXX01XVxfXXnstZ511Fh/5yEeIx+O86U1v4nOf+xz3338/Dz30EA8//DB//vOfufzyyyf4tW3bNtatWwcU7y2vXbuWwcFBXvWqV7F69erKhCef/vSned/73ldpv2nTJg499NDK70cffTQLFizg5z//eWXb9ddfz5lnnklra6v2lYDDDjuM7du3V+YkqIbuMdf0A3cul2PTpk1s2LCBjRs3Vl5sz2azbNy4kQ0bNrBp0ybtJwW9PllYz35sfNLTNmzMUVCfhjWh5+ooOLS0tHDZZZfR0dFBV1cX++23H6eeeiqzZs2ip6eHT3ziE9xzzz1j2px//vn84z/+I11dXbzjHe/goYceAqCtrY3nn3+eJ554gkgkwrJlyyZ9QOvGG2/k05/+NC960Ys44IADuOSSS7j++usn9QtgxYoVrF69mtbWVt7+9rezZ88ePvaxj9HW1sbpp5/O9u3bx0yi09PTM6bPc845hx/+8IdAcXKd2267jXPPPddT3sraoycPGo/uMdf0A3dHRwerV6+u3L8oP8BQ/t7f38/xxx9PR0eHlq7XdzB12+nYB/m9UL+wMUd++WS6HxN6ro6CwwEHHDBmFatkMsn73vc+DjzwQHp7e1m5ciVDQ0Njrl7Onz+/8vOsWbMq99DXrl3L6tWrOf3001m4cCEf+chHGBkZqdrvM888w4EHHlj5/cADD+SZZ56Z1C+AF7/4xZWfu7q62H///Sv35MuDe9mXuXPnMjzuNsPatWv53e9+xzPPPMMtt9zCS1/6Ul71qlfVkKWJlLWneqZK+36+J08CRnt7O0cfffSEyxHl7W1tbdqaXucX1m2nYx/EOY/9xsYc+eWT6X5M6Lk6Cg7jB5evfvWr/O1vf+OBBx4gFovx+9//HqCmN3ba2tq45JJLePzxx/njH//IHXfcUTnDHc/ChQvZsWNH5fedO3eycOHCSf3S5fDDD+fvf//7mG0HHnggK1as4IYbbuD666/3fLYNxVnylixZMuUrX9rHs2dvAkYikSCdThOJRGhpaSESiZBOpz2vzuMu8QUTG3PkLpXXv52rI7OU53jv6upizpw57Nu3j8suu6zm9r/73e/YtGkT+Xye3t5e2traJh28zjjjDC6//HL27NnD3r17+cxnPsPZZ59d1ScvnHjiiRMu8UPxIbWrrrqKP/zhD5x11lme+7nnnns44YQTprRxl8onYWBgAKUUS5Ys4aSTTmLJkiUopTw/Xe4eqgkmNubIPZxW/3aujszS0tLCxRdfTCqVYv/99+c1r3kNxx9/fM3td+/ezWmnnUZvby+HHXYYxxxzDGvXrq1q+8lPfpLly5dz+OGH88pXvpJXv/rVfPKTn6zqkxfWrFnDli1bxlx+Bzj11FPZt28fb3zjG1mwYIHnfm6++eYxD8NVQ/eYC80iI7t37waK91zi8Tjd3d1jtulS1qh3Ox37Wm3DvMiI1/1WT/zyyXQ/JvTCWkebN2/msMMO02rjx3vctVJejcsmZuLTunXrePzxx/nGN75htJ/bb7+d66+/np/85CeT2mzevJnFixdPOOamWmQkNBOwjH9IYvw2XdzEEcHExhy5CVjq364Z6sivQbkWbHwOYCY+vfe9761LP2vWrGHNmjXT2rkJWGpg/BOEfmrottOxNxFXs2NjjvzyyXQ/ro7Ci+68F37gl0/16Ef3mAvlwN3X19cwDd12OvYm4mp2bMyRXz6Z7sfVUXhxy3qaRfeYC+XAPdWL8PXW0G2nY28irmbHxhz55ZPpflwdhRcbn7wP6tsZoH/MhXLgnsniIjPV0G2nY28irmbHxhz55ZPpflwdhRd3xm0W3WMulAO3O1MILzbmyJ1x17+dqyOzuDNuszTVGbeIrBGRdYODg2SzWdLpNKlUipGRERKJBPl8nuHhYZRSRKNR4IUERKPRyiQB+XyeRCLByMgIqVSKzs5OstksyWSSXC5HPB6nUChM0Ch/j8ViFAoF4vF4RaOrq4tMJlPxp6xRnkh+vEY0GqVQKNDa2koulyOZTNYU05w5c2qKKZ1OM2vWrBnFlMlkPMUUj8e1Yqp1P6XT6Rnvp/Extba2WhdTb2+vL/tJ99ibLqbZs2fPeD+VNXRjEhGt/dTX11fzfuro6KjLsTc+pvIAMPq7Uop8Pl/5XigUxnyN/kwpVTcNYEqNlpaWCVqNjikSicwopmr+VNMAjMfU1tY24W/EVITmPe7RxGKxKaefq6eGbjsd+1ptw/wet4l9bxq/fDLdj6sjf9/jtolme4+7kf1s3ryZ/v7+CcfcVO9xW33GXS9MTELhVUO3nY69bROL2IiNOfLLJ9P9uDoKL7a8x71nzx6WLl1KKpWqyafPf/7zXHDBBTVpn3rqqdx5550Tttcjdt1jzo7s+8zoBdL91tBtp2NvIq5mx8Yc+eWT6X5cHTUOpRS7du3yPD/3dCxZsoSuri66u7uZP38+5513XmU1LXhhdcVaufbaaxERvvSlL43ZvmjRIu6+++5p22/fvh0RmXB/+Ytf/CLnnXceXV1dFAoFVq1aRWdnJ93d3fT19bFy5Uo2bdpUsf/4xz/ONddcw7333kt3dzfd3d3Mnj0bEan83t3dzc6dO/noRz9adWpV3dhrQfeYC+XArbuEp0kN3XY69ibianZszJFfPpnux9VR44hGo9x7772Ve+714Pbbbycej/PQQw+xceNGvvCFL1Q+87Ii17x58/jSl75kbIKbTCbDddddV1lwpOzTVVddRTweZ9++faxatarqHOgrVqwgHo8Tj8d57LHHgOLzCuVtL3nJSzjqqKOIxWKMv01bj2VfdY+5UA7cblWj8GJjjoL6NKyro8axc+dOAM+LJOkwf/58Vq9ezUMPPQTA/fffz+tf/3rmzJnDEUccMeaM+dprr+Xggw+mp6eHgw46iBtvvLHy2WGHHcZrX/tavva1r1Xtp1Ao8MUvfpGXvvSl7LfffrzjHe9g3759AKxcuRIovjbV3d3Nn/70Jx544AHmzJnDokWLqupFIhFOP/10Hn/88cq2Sy+9tOrKYpOxatUqfvGLX9Rs7xW3OlgNuHWEw4uNOXLrcde/XdDrKJ/Ps337drZu3crWrVvZvn07ANu2bRuzrR7TcT711FPceeedHHLIITz99NOcdNJJfPzjH2ffvn185Stf4dRTT2XPnj0kEgkuvPBC7rzzToaHh/njH//IkUceOUbrs5/9LN/4xjcqA/Jo/uM//oNbb72Ve+65h2eeeYa5c+fyr//6rwCVtb7LZ8Wvfe1r2bRpE4ceeuikfmezWW688UZe85rXeI79sMMO4+GHH/bcvlbcetwOh8PRZORyOTZt2sSGDRvYuHFj5XWhbDbLxo0b2bBhA5s2bTJ6teCUU06hp6eHxYsX86IXvYjLLruMG264gRNPPJETTzyRlpYWjj32WJYvX84vf/lLoDgAPfroo6RSKRYsWMDLX/7yMZpHHnkkxx57LFdcccWE/q6++mo+97nPsWjRIjo6Orj00ku55ZZbJo1paGiInp6eCdsvvPBC5syZQ09PD1dddRWXXHKJ5xz09PRY+V5/KAduEw8XeNXQbadjX4+HJpoNG3Pkl0+m+3F15B8dHR2sXr2a/v5+4AUfy9/7+/s5/vjjjd6fv/XWWxkeHubuu+9my5Yt7N27lx07dvDTn/6UefPmMWfOHObMmcN9993Hrl27mD17Nj/+8Y+5+uqrWbBgASeddBJbtmyZoPuZz3yGb3/72zz77LNjtu/YsYO3vvWtFd3DDjuMSCQywa7M3Llzq94vv/LKKxkaGiKVSnHHHXdw2mmn8cgjj3jKwfDwsC8z6ekec6EcuE1MWedVQ7edjr2N0xDaho05CupUja6O/KW9vZ2jjz6a9vb2qtvb2trq0u8xxxzDeeedx4c//GEWL17M2rVr2bdvH0NDQwwNDZFIJPjYxz4GwOrVq/n1r3/Nrl27WLp0Ke95z3sm6C1dupS3ve1tfO5znxuzffHixdx5550V3aGhIdLpNP39/VUfCDv88MP5+9//PqnfLS0trFixgkMOOYT169d7in3z5s0cccQRntrqoHvMhXLgzmQyDdPQbadjbyKuZsfGHPnlk+l+XB35TyKRIJ1OE4lEaGlpIRKJkE6nSSQSde334osv5te//jVHH300t99+O7/61a/I5/Ok02nuvvtunnrqKZ599lluu+02EokEHR0ddHd3T3rv9pJLLuEHP/jBmMvQ73//+/nEJz7Bjh07gOI72rfddhsABxxwAC0tLWzdurVif9RRRzE0NMTTTz8NUPXVuD/96U88/vjjEy7Z18o999zDCSecMGZbPV7B0z3mQjlw6y5ablJDt52OvYm4mh0bc+SXT6b7cXXkPwMDAyilWLJkCSeffDJLlixBKVX3p8sPOOAAzjnnHK688kpuu+02rrjiCg444AAWL17Ml7/85cpUn1/72tdYuHAh8+bN45577uHb3/52Vb2DDjqItWvXjvmH46KLLuItb3kLxx13HD09PbzmNa/hgQceAIr75BOf+ASve93rmDNnDvfffz/t7e2cd9553HDDDcALD3h98IMfrLyPvXbtWi6//PIJg28t/OUvf6G7u5ujjjpqzPZ6PLyoe8y5KU991gj6VI1Bx0156qY8NWXbiClPd+/eDRRf0ZpqW72xZcrTPXv2sGLFCjZu3Eh7e7tRn0499VTe/e53c+KJJ47ZbsOUp3bezKkzJv54edXQbadjb9uAZCM25sgvn0z34+rIf6oNzn4O2GVsGLSheCWg2gNwJvjZz35WdXs9Ytc95kJ5qdwtRxhebMyRW9az/u1cHZnFxklqgjqRETTZsp71wsTj/V41dNvp2Pvx2kLQsTFHfvlkuh9XR+HFxifvg/p2Bugfc6EcuN2ZQnixMUfujLv+7VwdmcWdcZvFnXHXgDtTCC825sidcde/nasjs7gzbrO4M+4aMLGijlcN3XY69vVcKahZsDFHfvlkuh9XR+HFnXGbRfeYC+XAXW1+W780dNvp2JuIq9mxMUd++WS6H1dH4cWWp8pH45dP9ehH95gL5cCtu2i5SQ3ddjr2JuJqdmzMkV8+me7H1VF4sXE+96DO+Q/6x1woB+7Ozs6Gaei207E3EVezY2OO/PLJdD+ujsLLVLOHrVq1imuuuaZmrZ07d9Ld3T3pkqS1rqEd1OVxQf+Ys3rgFpE1IrJucHCQbDZLOp0mlUoxMjJCIpEgn88zPDyMUqpyj6D8dF40GkUpxfDwMPl8nkQiwcjICKlUiuHhYbLZLMlkklwuRzwep1AoTNAof4/FYhQKBeLxeEUjHo+TyWQq/pQ1YrFYVY1oNEqhUGBwcJBcLkcymawppmw2W1NM6XSaeDw+o5gymYynmOLxuFZMte6ndDo94/00PqahoSHrYkqn077sJ91jb7qYEonEjPdTMpn0FNPzzz+vtZ8ymUzN+ykWi9Xl2BsfU/le6ejvSiny+Xzle3kq0fJXPp8nkcrzqz8Oc/P6KHf+MUYyXdDWGG1TzY8lS5Zw1113jdH4/ve/z+tf//qKxnit0Rqjf5/On/7+/soqX9X8KZ/hThfTaH+qxTSdRrX8zGQ/TZfj0Z9Ho9EJfyOmIpRTnmaz2Qkr7PilodtOx75W2zBPeWpi35vGL59M9+PqyN8pT5VS3Lw+xvW/jNHSAtkRRXubUCjA2hN7OeO43qqraHlhyZIlXHPNNbzpTW+qbLv22mu55ppruO+++ygUCpOeea5atYqzzz6bCy64YNp+crnctE9pX3rppTzxxBOVOcknYyqfTGK6n82bN/PSl750wjE31ZSnVp9x1wu3jnB4sTFHQb035+rIX25eH+P6O2NkRhSpjCJfgFRGkRlRXH9njJvXx3zx48tf/jKnnXbamG0XXnghF110UeX3J598kqOOOore3l5OPvlk9u3bB8D27dsREb73ve/xkpe8hDe84Q2VbeWz0W3btnHMMcfQ09PDsccey969e32Jq5G49bhrwMRVBq8auu107INw9aTR2Jgjv3wy3Y+rI/9Ipgtc/8sYmWx13zLZ4uCdStf/n46zzz6bu+66q3KbIJfL8aMf/YhzzjmnYvPDH/6Q73//++zatYvW1lYuvPDCMRr33HMPmzdv5q677pqgf+aZZ7Js2TL27t3Lpz71Ka677rq6xmMDusdcKAduEy/Qe9XQbadjb+OkCLZhY46COnGEqyP/uO+hJNNdnW0RuPfhlLE+TznlFObMmVP5+pd/+RcAFixYwIoVK/jpT38KwK9+9Sv2339/li1bVmm7du1aXvGKVzB79mw++9nP8pOf/GTMw2eXXnops2fPpqura0yfO3fu5C9/+Quf/exn6ejoYOXKlaxZs8ZYTLaie8yFcuCe7sZ/PTV02+nYm4ir2bExR375ZLofV0f+sS9WIDsy9VlZNqfYF63+ZLYXbr31VoaGhipf3/rWtyqfnXPOOZV7zjfccANr164d03bx4sWVnw888EBGRkbGXPIe/flonnnmGebOncvs2bPHtK+FoF65Av1jLpQDt3uNJbzYmCP3Olj92wW9jub1ttDeNvWDZ+2twrw+fyYheetb38ojjzzCo48+yh133MFZZ5015vOBgYHKzzt37qStrY3999+/sm2yh+gWLFjA4OAgiURiTPtacK+DNTlu4ojwYmOO3AQs9W8X9Dp6/ZGzmO75pYKCFUd0TW1kiPb2dk477TTOPPNMjjrqKF7ykpeM+fyGG27g8ccfJ5lM8ulPf5rTTjutphnHDjzwQJYvX84ll1xCNpvlvvvu4/bbb6/Jp6A+5AluApaa6O7ubpiGbjsdexNxNTs25sgvn0z34+rIP2Z1trD2xF462qufqXa2C2tP6KWr07+zznPPPZdNmzZNuEwOxXvc5513HvPnzyedTnPllVfWrH3TTTfxwAMPMG/ePC677LIxD71N55Mf1KMf3WMulO9xR6NR+vr6GqKh207HvlbbML/HbWLfm8Yvn0z34+qowe9x5xTtrfV5j3s6crkczzzzDEuXLmX37t309vb60u90PvnxYKHpfjZv3szChQsnHHNTvcdt3+OTPmDij5dXDd12Ova2DUg2YmOO/PLJdD+ujvxFRDhzdR+nHNPDfQ+n2BfNM68vwoojunw70y7T0tLC1772NU4//XQrBm0I7tsZoH/MhfJSue6i5SY1dNvp2JuIq9mxMUd++WS6H1dHjWFWZwvH/fNsTj+ul+P+ebbvg3YikaC3t5df//rXXHbZZb72PRVBXtZT95gL5Rm37qLlJjV02+nYm4ir2bExR375ZLofV0fhZPbs2cTj8Ua7MYEgn3HrHnOhPOPWXbTcpIZuOx17E3E1OzbmyC+fTPfj6ii8+HV2q0OQz7h1j7lQDtwm7sl41dBtp2Nvy70mm7ExR375ZLofV0czIwgPBk9GLa92+Y1fPpnsp3wM6B5zoRy4TVzm8aqh207H3sbLV7ZhY4788sl0P66OvNPZ2cnzzz8f2MHbxoVYgvYet1KK559/ns7OTu1jLpT3uGfNmtUwDd12OvYm4mp2bMyRXz6Z7sfVkXcWLVrEU089xZ49e+reVz1QSvn26lmt+OWTyX46OztZtGiR9rvhoRy40+n0mLlw/dTQbadjbyKuZsfGHPnlk+l+XB15p62tjYMOOqiufdSTRCJhXR355VM9+tHVrNvALSKHAj8etelg4NPAHOA9QPlfzY8rpX5ZLz+qMX7Bcj81dNvp2JuIq9mxMUd++WS6H1dH4cXGHAW1jrxo1u0et1Lqb0qpI5VSRwLLgCTwn6WPv17+zO9BG8w8FehVQ7edjr2NT3raho05CurTsK6OwouNOQpqHXnR9OvhtDcCTyqldvjU35SYuD/hVUO3nY69bfecbMTGHPnlk+l+XB2FFxtzFNQ68qLp18B9OnDzqN8/KCKPiMj3RWSuTz5UMDFJvFcN3XY69n5Nsh9kbMxRUBdHcHUUXmzMUVDryItm3RcZEZF24Bng5UqpZ0XkxcBeQAGfBRYopd5Vpd17gfeWfj0U+JtBt/qAmc6y4FVDt52Ofa22+1PcB2HExL43jV8+me7H1ZGrI5sIah1NpnmgUuqAqtZKqbp+AScD6yf5bAnwaL19qNLvukZp6LbTsa/VFtjgd85t+TKx74Pqk+l+XB25OrLpK6h15EXTj2sLZzDqMrmILBj12VuBR33wYTy1rcxeHw3ddjr2JuJqdmzMkV8+me7H1VF4sTFHQa0jbc26XioXkdnATuBgpVS0tO164EiKl8q3A+9TSu2qmxOOCYjIBjXJOq8Oh6M2XB05GkVdJ2BRSiWA/cZtW1vPPh01sa7RDjgcTYCrI0dDqPvDaQ6Hw+FwOMxh3zP9DofD4XA4JsUN3A6Hw+FwBAg3cDscDofDESBCuTqY4wVE5BTgJKAX+J5San1jPXI4goeIHAZcRHFSlt8qpb7dYJccTYw7425CSlPJPicij47bfryI/E1EnhCRjwEopW5VSr0HeD/wzkb463DYiGYdbVZKvR94B/C6RvjrCA9u4G5OrgWOH71BRCLA/wNOAF4GnCEiLxtl8snS5w6Ho8i1aNSRiLwF+AXg+4qHjnDhBu4mRCn1e2DfuM1HAU8opbYqpbLAj4CTpcgVwJ1Kqb/67avDYSs6dVSy/y+l1AnAWf566ggb7h53eOgHBkb9/hTwz8D/Bt4E9InIIUqpqxvhnMMREKrWkYisAt4GdODOuB11xg3cIUcpdSVwZaP9cDiCjFLqbuDuBrvhCAnuUnl4eBpYPOr3RaVtDoejdlwdORqOG7jDw1+AfxCRg0prpJ8O/FeDfXI4goarI0fDcQN3EyIiNwN/Ag4VkadE5N1KqRzwQeAuYDPwE6XUY4300+GwGVdHDltxi4w4HA6HwxEg3Bm3w+FwOBwBwg3cDofD4XAECDdwOxwOh8MRINzA7XA4HA5HgHADt8PhcDgcAcIN3A6Hw+FwBAg3cDusQESuFJGHSl/Z0rKJ5d+7Jmlzkoh8pgbtu0XkNPNeV+2rT0T+20O7t4jIlFPPishCEfmjd+/sRkQuEJF/0WyzSkRSpePkRYb9OU9EoqOOw/LXW0r9PjpF24NF5Eci8qSIPCwifxaRd03i98bS9w0ismYKzUjJLu7X8eywEzdXucMKlFIXln8Wke3AWUqpDdM0+ydgXj398sBciitIaaGU+i+mmYFLKfUMcLRHv4LA64FJB8MpeFIpdaRhX8rcq5R68/iNpUVFqiIiS4B7KC6Ve4ZSSolIP3CjiPyDUur/lkzH+C0iRwB/EJGDlFJ7xusqpfLAkSJyt/dwHM2AO+N2WI+IfEpEHheRR0TkFhGZLyL/DLwfeKeIfE5EZovID0XkfhH5u4g8KCKHTqN7t4h8q3Q2tFVELittXyIiAyKyvqS1QESOFpF7ReSvpTOjCX/MS/wA6CqdGUVEJCMiPyldQVguIu8SkQdKZ1k7ROQDpT7PE5E7Rvn1BRH5vYhsF5HrRaSl5Fe8ZHOpiFwrIneJyJaSbwtLnx1V8vEREfnPks+rqsT/jyLyOxF5TEQ2icg7S9tfXtr+SOls8ZzS9lUi8icR+Vmpz7+KyBoR+bWI7BSRr4+y+4uI3FrSeEBEDit9dq2IfHiUD9eKyIdF5K3AW4APici/lj77RKmPh0paC2s4Vg4r7bcHS+3eVdp+bmkfd5eOlc0ick4pr98s+fh4afvrputnGv4vcJNS6jpVmuFKKfU08E7gIhFZUK2RUuphIAkcKCJLReQPpTj+KppXIhxNjlLKfbkvq76A7cDy0s/nA38EZpd+vxT41aifryr9fBpw5SiNq4H/KP18N3BalX7uBn4BtAFzgC3Am4ElgAJWlOzmAn8DlpR+X0hxaceXVNFcAsRH/a6AtaWfuylOoblf6ffXAMOln88D7hjl108o/mPdQ3ERi/81WrsU+5NAb+n3/wIuo3gVbQA4obT9fwEFYFUVX/8K/Evp58VlvdL3t42K9SngtcAqIAe8qvTZnaV90w7sD2RL9quA/Kj8vR/YUPr5WuDDo3yo/D7u53MornXdWvr9vcAvq8SwCni09HMr8Bjw6tLvfcDjwGtKv98IfAv4HrCutO21wE+BltLvHwNuH7VPosBDo76uHt9vFZ82ASdN8tmDwFurtae4LOhuYFbJx4+Vts8v5aLs491UOZ7dV3i+3KVyh+2cAPxAKZUo/f5N4BNSXOChglLqltIZ1f8GDqH4h/FPNeh/Ryk1AgyJyE+B1RQv1+ZGtX8tsAC4VUQqXQKHAztr6OPeko/x0pn6SSLyD8CRFAfzatyulCoAwyLyBMVbAtvG2dytlIqVft5Ysnllqa87S99/J1XuxYrIPOAI4JqS3QDwUhF5GdCplPp5afszIvIz4Hjgd8A2pdTGksyTQFQplQX2ikiMF25dPKyUurf08/eB/yci+02XqFG8meIthw2lnEcoDmhT8Y/AS4Hvj9pPXcCrgPsp/gPxMJAClpXi+5OIfBJ4n4i8lOJxMzxKs+ql8hpom2R7B8VjB4r5fmiU/QBwslIqKSL/CfxQRI4CfgNcWDoeHA43cDusZ/ztnBaKx62M3li65Pxe4CrgJmAfcFAN+rlx2vnSzxlVXFACioPGZqXUP4/qbyGwR0SuAZaXNl8N/KpKH+XL24so/jOwDrgPuIXiAFWN1KifFePincImV8U2z0TKsVUWK5DirYVIFdsWXhiIMuM+G6liP1qfkj9S8mN8LGP+ARtFBLhCKfXtkm8dFK98TEUEGFJj7xu/mOJZM8CLgU6Kg+dCYKuInETxn8GvArdRvOpy9jT9TMcfKP4DcOvojVK8z30Q8GeK/2RMem9eKXVH6Z+7Y4E3ApeIyNFKqSdn6JujCXD3uB22cxdwvojMLv1+IfB7pVSG4uBQHlBWA9cqpb5H8bL2GqoPQuM5u3Sfcy7wDuD2Kjb3U1zKcSWAiBwJ/A+wUCl1gVLqyNLX1SWfIjLqlG8Uy4E9wOVKqbsoDdoiUouftbIZyIjI8SXtoyiehY9ZTah0pv4gcG7JbjHFAWcIyIrI20rbFwKnAr/W9ONIETm89PN7gT8opYYoxr+8pL0/sGJUm9H78y7gAhHpLf3+GeD6afr8G5AWkbNHxfQosExE2oCbgU9TvKVwc2nbsRSvbnyb4pKdp1DbcTMVnwPeISLnljeU/mm7GfiWKj5kOCUichPwTqXUj4B/AWKMXQfcEWLcGbfDdr5H8Q/Wn0WkBXgCOKv02W+Bn4tIFvgKsE5Ezqd4ZvcgpcvG09BF8Qyoh+If1d9K8angCkqpPSJyKvBlEemk+A/vWqXUjip6uyjeO672kNN64F3A30QkUep3D8VL+0ZQSuVKvl4tIl8A/k7xvmmyivmZwLdKtxcUcIFSakBETgGuFJFLKf6N+EzpkvsqDVd2A58r5fI5YG1p+39QfLr6bxSfZbh7VJs7gatK//NcAfQD94uIonhL4rypOlRKZUXkZOCbIvIRiv8EfEop9QcR+TKwWyl1DUApxs9RvEpyk4g8QvG4+T1waulYm47DpPSw4Cj6Szl8LXC5iHyc4j8kaYpXWtbVoAvwWeAaEXlfya//pPikusPhlvV0hBcpvlZzlVLqlkb7YpLSIPUVpdSzpbPOh4GDS2e8fvS/imJeX9FMfdlCsx63jtpxZ9wOR/OxA/itiIxQvJ98gV+DdoMoP+R1nFLquUY7Uy9Kt1QexOAVGkcwcWfcDofD4XAECPdwmsPhcDgcAcIN3A6Hw+FwBAg3cDscDofDESDcwO1wOBwOR4BwA7fD4XA4HAHCDdwOh8PhcASI/w+xgTyfm4VM1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_data = {\n",
    "    'Transform(ViT)': {\n",
    "        \"vit-B_32_7\": {55: 80.73},\n",
    "        \"ViT-B_16_7\": {224: 84.15},\n",
    "        \"ViT-L_32_7\": {196: 84.37},\n",
    "        \"ViT-L_16_7\": {783: 86.30},\n",
    "        \"ViT-L_16_14\": {1567: 87.12},\n",
    "        \"ViT-H_14_14\": {4262: 88.08}\n",
    "    },\n",
    "    'ResNet(BiT)': {\n",
    "        \"ResNet50x1_7\": {50: 77.54},\n",
    "        \"ResNet50x2_7\": {199: 82.12},\n",
    "        \"ResNet101x1_7\": {96: 80.67},\n",
    "        \"ResNet152x1_7\": {141: 81.88},\n",
    "        \"ResNet152x2_7\": {563: 84.97},\n",
    "        \"ResNet152x2_14\": {1126: 85.56},\n",
    "        \"ResNet200x3_14\": {3306: 87.22}\n",
    "    },\n",
    "    'Hybrid': {\n",
    "        \"R50x1+Vit-B_32_7\": {106: 84.90},\n",
    "        \"R50x1+Vit-B_16_7\": {274: 85.58},\n",
    "        \"R50x1+Vit-L_32_7\": {246: 85.68},\n",
    "        \"R50x1+Vit-L_16_7\": {859: 86.60},\n",
    "        \"R50x1+Vit-L_16_14\": {1668: 87.12}\n",
    "    }\n",
    "}\n",
    "\n",
    "accuracy_model_flops_chart(accuracy_data=accuracy_data,\n",
    "                           ylim=[75, 90],\n",
    "                           figsize=(8, 6),\n",
    "                           title='ImageNet',\n",
    "                           xlabel='Total pre-training compute [exaFLOPs]',\n",
    "                           ylabel='Transfer accuracy [%]')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pos_embedding_cosine_chart\n",
    "\n",
    "函数`pos_embedding_cosine_chart`主要用于绘制图像patches之后的位置编码之间的余弦相似度。\n",
    "\n",
    "#### 数据下载\n",
    "\n",
    "本案例需要用到ViT-B_32模型预训练好的位置编码，该数据已处理为ndarray数据结构，维度为(1, 50, 768)。\n",
    "\n",
    "> [数据链接地址](https://gitee.com/mindspore/vision/blob/master/docs/data/pos_embedding.npy)\n",
    "\n",
    "#### 参数说明：\n",
    "\n",
    "- `pos_embedding`：位置编码。\n",
    "- `save_path`：图表的保存路径。\n",
    "- `title`：图表的标题。\n",
    "- `xlabel`：图表x坐标的标签。\n",
    "- `ylabel`：图表y坐标的标签。\n",
    "- `colorbar_label`：图表彩条的标签。\n",
    "\n",
    "#### 样例\n",
    "\n",
    "以下样例绘制了ViT-B_32模型预训练好的位置编码之间的余弦相似度。下载好的位置编码的维度为(1, 50, 768)，该函数会先将数据维度进行压缩，然后去除掉最开始用来分类token的位置编码，只保留图像patches之后的位置编码，然后进行图像绘制。结论如下：\n",
    "\n",
    "1. 每一个位置编码都和自己的同一行或者同一列有较高的余弦相似度，说明图像的同一行或者同一列具有较强的位置关系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 98 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_embedding = np.load('./data/pos_embedding.npy')\n",
    "pos_embedding_cosine_chart(pos_embedding=pos_embedding,\n",
    "                           title='Position embedding similarity',\n",
    "                           xlabel='Input patch column',\n",
    "                           ylabel='Input patch row',\n",
    "                           colorbar_label='Cosine similarity')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}